The methods discussed so far use a static task distribution for learning with tasks sampled i.i.d from this distribution for training.
Curriculum learning [1]}, [2]} instead posits that choosing the order in which instances are presented, with gradually increasing complexity, can enable faster learning and better generalization.
We explore whether a curriculum in task sampling is beneficial for meta-learning by proposing a method to sample increasingly difficult tasks during training.
To enable this we need a method to propose difficult tasks based on the current state of the model during training.
